{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 8: Conditional GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.layers import (Activation, BatchNormalization, Concatenate, Dense,\n",
    "                          Embedding, Flatten, Input, Multiply, Reshape)\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.layers.convolutional import Conv2D, Conv2DTranspose\n",
    "from keras.models import Model, Sequential\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rows = 28\n",
    "img_cols = 28\n",
    "channels = 1\n",
    "\n",
    "# Input image dimensions\n",
    "img_shape = (img_rows, img_cols, channels)\n",
    "\n",
    "# Size of the noise vector, used as input to the Generator\n",
    "z_dim = 100\n",
    "\n",
    "# Number of classes in the dataset\n",
    "num_classes = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_generator(z_dim):\n",
    "\n",
    "    model = Sequential()\n",
    "\n",
    "    # Reshape input into 7x7x256 tensor via a fully connected layer\n",
    "    model.add(Dense(256 * 7 * 7, input_dim=z_dim))\n",
    "    model.add(Reshape((7, 7, 256)))\n",
    "\n",
    "    # Transposed convolution layer, from 7x7x256 into 14x14x128 tensor\n",
    "    model.add(Conv2DTranspose(128, kernel_size=3, strides=2, padding='same'))\n",
    "\n",
    "    # Batch normalization\n",
    "    model.add(BatchNormalization())\n",
    "\n",
    "    # Leaky ReLU activation\n",
    "    model.add(LeakyReLU(alpha=0.01))\n",
    "\n",
    "    # Transposed convolution layer, from 14x14x128 to 14x14x64 tensor\n",
    "    model.add(Conv2DTranspose(64, kernel_size=3, strides=1, padding='same'))\n",
    "\n",
    "    # Batch normalization\n",
    "    model.add(BatchNormalization())\n",
    "\n",
    "    # Leaky ReLU activation\n",
    "    model.add(LeakyReLU(alpha=0.01))\n",
    "\n",
    "    # Transposed convolution layer, from 14x14x64 to 28x28x1 tensor\n",
    "    model.add(Conv2DTranspose(1, kernel_size=3, strides=2, padding='same'))\n",
    "\n",
    "    # Output layer with tanh activation\n",
    "    model.add(Activation('tanh'))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_cgan_generator(z_dim):\n",
    "\n",
    "    # Random noise vector z\n",
    "    z = Input(shape=(z_dim, ))\n",
    "\n",
    "    # Conditioning label: integer 0-9 specifying the number G should generate\n",
    "    label = Input(shape=(1, ), dtype='int32')\n",
    "\n",
    "    # Label embedding:\n",
    "    # ----------------\n",
    "    # Turns labels into dense vectors of size z_dim\n",
    "    # Produces 3D tensor with shape (batch_size, 1, z_dim)\n",
    "    label_embedding = Embedding(num_classes, z_dim, input_length=1)(label)\n",
    "\n",
    "    # Flatten the embedding 3D tensor into 2D tensor with shape (batch_size, z_dim)\n",
    "    label_embedding = Flatten()(label_embedding)\n",
    "\n",
    "    # Element-wise product of the vectors z and the label embeddings\n",
    "    joined_representation = Multiply()([z, label_embedding])\n",
    "\n",
    "    generator = build_generator(z_dim)\n",
    "\n",
    "    # Generate image for the given label\n",
    "    conditioned_img = generator(joined_representation)\n",
    "\n",
    "    return Model([z, label], conditioned_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_discriminator(img_shape):\n",
    "\n",
    "    model = Sequential()\n",
    "\n",
    "    # Convolutional layer, from 28x28x2 into 14x14x64 tensor\n",
    "    model.add(\n",
    "        Conv2D(64,\n",
    "               kernel_size=3,\n",
    "               strides=2,\n",
    "               input_shape=(img_shape[0], img_shape[1], img_shape[2] + 1),\n",
    "               padding='same'))\n",
    "\n",
    "    # Leaky ReLU activation\n",
    "    model.add(LeakyReLU(alpha=0.01))\n",
    "\n",
    "    # Convolutional layer, from 14x14x64 into 7x7x64 tensor\n",
    "    model.add(\n",
    "        Conv2D(64,\n",
    "               kernel_size=3,\n",
    "               strides=2,\n",
    "               input_shape=img_shape,\n",
    "               padding='same'))\n",
    "\n",
    "    # Batch normalization\n",
    "    model.add(BatchNormalization())\n",
    "\n",
    "    # Leaky ReLU activation\n",
    "    model.add(LeakyReLU(alpha=0.01))\n",
    "\n",
    "    # Convolutional layer, from 7x7x64 tensor into 3x3x128 tensor\n",
    "    model.add(\n",
    "        Conv2D(128,\n",
    "               kernel_size=3,\n",
    "               strides=2,\n",
    "               input_shape=img_shape,\n",
    "               padding='same'))\n",
    "\n",
    "    # Batch normalization\n",
    "    model.add(BatchNormalization())\n",
    "\n",
    "    # Leaky ReLU\n",
    "    model.add(LeakyReLU(alpha=0.01))\n",
    "\n",
    "    # Output layer with sigmoid activation\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_cgan_discriminator(img_shape):\n",
    "\n",
    "    # Input image\n",
    "    img = Input(shape=img_shape)\n",
    "\n",
    "    # Label for the input image\n",
    "    label = Input(shape=(1, ), dtype='int32')\n",
    "\n",
    "    # Label embedding:\n",
    "    # ----------------\n",
    "    # Turns labels into dense vectors of size z_dim\n",
    "    # Produces 3D tensor with shape (batch_size, 1, 28*28*1)\n",
    "    label_embedding = Embedding(num_classes,\n",
    "                                np.prod(img_shape),\n",
    "                                input_length=1)(label)\n",
    "\n",
    "    # Flatten the embedding 3D tensor into 2D tensor with shape (batch_size, 28*28*1)\n",
    "    label_embedding = Flatten()(label_embedding)\n",
    "\n",
    "    # Reshape label embeddings to have same dimensions as input images\n",
    "    label_embedding = Reshape(img_shape)(label_embedding)\n",
    "\n",
    "    # Concatenate images with their label embeddings\n",
    "    concatenated = Concatenate(axis=-1)([img, label_embedding])\n",
    "\n",
    "    discriminator = build_discriminator(img_shape)\n",
    "\n",
    "    # Classify the image-label pair\n",
    "    classification = discriminator(concatenated)\n",
    "\n",
    "    return Model([img, label], classification)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_cgan(generator, discriminator):\n",
    "\n",
    "    # Random noise vector z\n",
    "    z = Input(shape=(z_dim, ))\n",
    "\n",
    "    # Image label\n",
    "    label = Input(shape=(1, ))\n",
    "\n",
    "    # Generated image for that label\n",
    "    img = generator([z, label])\n",
    "\n",
    "    classification = discriminator([img, label])\n",
    "\n",
    "    # Combined Generator -> Discriminator model\n",
    "    # G([z, lablel]) = x*\n",
    "    # D(x*) = classification\n",
    "    model = Model([z, label], classification)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build and compile the Discriminator\n",
    "discriminator = build_cgan_discriminator(img_shape)\n",
    "discriminator.compile(loss='binary_crossentropy',\n",
    "                      optimizer=Adam(),\n",
    "                      metrics=['accuracy'])\n",
    "\n",
    "# Build the Generator\n",
    "generator = build_cgan_generator(z_dim)\n",
    "\n",
    "# Keep Discriminator’s parameters constant for Generator training\n",
    "discriminator.trainable = False\n",
    "\n",
    "# Build and compile CGAN model with fixed Discriminator to train the Generator\n",
    "cgan = build_cgan(generator, discriminator)\n",
    "cgan.compile(loss='binary_crossentropy', optimizer=Adam())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracies = []\n",
    "losses = []\n",
    "\n",
    "\n",
    "def train(iterations, batch_size, sample_interval):\n",
    "\n",
    "    # Load the MNIST dataset\n",
    "    (X_train, y_train), (_, _) = mnist.load_data()\n",
    "\n",
    "    # Rescale [0, 255] grayscale pixel values to [-1, 1]\n",
    "    X_train = X_train / 127.5 - 1.\n",
    "    X_train = np.expand_dims(X_train, axis=3)\n",
    "\n",
    "    # Labels for real images: all ones\n",
    "    real = np.ones((batch_size, 1))\n",
    "\n",
    "    # Labels for fake images: all zeros\n",
    "    fake = np.zeros((batch_size, 1))\n",
    "\n",
    "    for iteration in range(iterations):\n",
    "\n",
    "        # -------------------------\n",
    "        #  Train the Discriminator\n",
    "        # -------------------------\n",
    "\n",
    "        # Get a random batch of real images and their labels\n",
    "        idx = np.random.randint(0, X_train.shape[0], batch_size)\n",
    "        imgs, labels = X_train[idx], y_train[idx]\n",
    "\n",
    "        # Generate a batch of fake images\n",
    "        z = np.random.normal(0, 1, (batch_size, z_dim))\n",
    "        gen_imgs = generator.predict([z, labels])\n",
    "\n",
    "        # Train the Discriminator\n",
    "        d_loss_real = discriminator.train_on_batch([imgs, labels], real)\n",
    "        d_loss_fake = discriminator.train_on_batch([gen_imgs, labels], fake)\n",
    "        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
    "\n",
    "        # ---------------------\n",
    "        #  Train the Generator\n",
    "        # ---------------------\n",
    "\n",
    "        # Generate a batch of noise vectors\n",
    "        z = np.random.normal(0, 1, (batch_size, z_dim))\n",
    "\n",
    "        # Get a batch of random labels\n",
    "        labels = np.random.randint(0, num_classes, batch_size).reshape(-1, 1)\n",
    "\n",
    "        # Train the Generator\n",
    "        g_loss = cgan.train_on_batch([z, labels], real)\n",
    "\n",
    "        if (iteration + 1) % sample_interval == 0:\n",
    "\n",
    "            # Output training progress\n",
    "            print(\"%d [D loss: %f, acc.: %.2f%%] [G loss: %f]\" %\n",
    "                  (iteration + 1, d_loss[0], 100 * d_loss[1], g_loss))\n",
    "\n",
    "            # Save losses and accuracies so they can be plotted after training\n",
    "            losses.append((d_loss[0], g_loss))\n",
    "            accuracies.append(100 * d_loss[1])\n",
    "\n",
    "            # Output sample of generated images\n",
    "            sample_images()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_images(image_grid_rows=2, image_grid_columns=5):\n",
    "\n",
    "    # Sample random noise\n",
    "    z = np.random.normal(0, 1, (image_grid_rows * image_grid_columns, z_dim))\n",
    "\n",
    "    # Get image labels 0-9\n",
    "    labels = np.arange(0, 10).reshape(-1, 1)\n",
    "\n",
    "    # Generate images from random noise\n",
    "    gen_imgs = generator.predict([z, labels])\n",
    "\n",
    "    # Rescale image pixel values to [0, 1]\n",
    "    gen_imgs = 0.5 * gen_imgs + 0.5\n",
    "\n",
    "    # Set image grid\n",
    "    fig, axs = plt.subplots(image_grid_rows,\n",
    "                            image_grid_columns,\n",
    "                            figsize=(10, 4),\n",
    "                            sharey=True,\n",
    "                            sharex=True)\n",
    "\n",
    "    cnt = 0\n",
    "    for i in range(image_grid_rows):\n",
    "        for j in range(image_grid_columns):\n",
    "            # Output a grid of images\n",
    "            axs[i, j].imshow(gen_imgs[cnt, :, :, 0], cmap='gray')\n",
    "            axs[i, j].axis('off')\n",
    "            axs[i, j].set_title(\"Digit: %d\" % labels[cnt])\n",
    "            cnt += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Model and Inspect Training Progress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `'Discrepancy between trainable weights and collected trainable'` warning from Keras is expected. It is by design: The Generator's trainable parameters are intentionally held constant during Discriminator training, and vice versa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/keras/engine/training.py:478: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 [D loss: 0.039132, acc.: 98.44%] [G loss: 3.471655]\n",
      "2000 [D loss: 0.000817, acc.: 100.00%] [G loss: 0.224581]\n",
      "3000 [D loss: 0.000251, acc.: 100.00%] [G loss: 9.038122]\n",
      "4000 [D loss: 0.303368, acc.: 90.62%] [G loss: 2.383583]\n",
      "5000 [D loss: 0.029795, acc.: 100.00%] [G loss: 2.305811]\n",
      "6000 [D loss: 0.048169, acc.: 100.00%] [G loss: 2.465776]\n",
      "7000 [D loss: 0.261222, acc.: 85.94%] [G loss: 5.283850]\n",
      "8000 [D loss: 0.290597, acc.: 85.94%] [G loss: 1.537661]\n",
      "9000 [D loss: 0.150554, acc.: 96.88%] [G loss: 4.684689]\n",
      "10000 [D loss: 0.029428, acc.: 100.00%] [G loss: 4.894794]\n",
      "11000 [D loss: 0.020208, acc.: 100.00%] [G loss: 4.035442]\n",
      "12000 [D loss: 0.013338, acc.: 100.00%] [G loss: 4.429784]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set hyperparameters\n",
    "iterations = 12000\n",
    "batch_size = 32\n",
    "sample_interval = 1000\n",
    "\n",
    "# Train the CGAN for the specified number of iterations\n",
    "train(iterations, batch_size, sample_interval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output from a Trained CGAN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 50 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set grid dimensions\n",
    "image_grid_rows = 10\n",
    "image_grid_columns = 5\n",
    "\n",
    "# Sample random noise\n",
    "z = np.random.normal(0, 1, (image_grid_rows * image_grid_columns, z_dim))\n",
    "\n",
    "# Get image labels to generate: 5 samples for each label\n",
    "labels_to_generate = np.array([[i for j in range(5)] for i in range(10)])\n",
    "labels_to_generate = labels_to_generate.flatten().reshape(-1, 1)\n",
    "\n",
    "# Generate images from random noise\n",
    "gen_imgs = generator.predict([z, labels_to_generate])\n",
    "\n",
    "# Rescale image pixel values to [0, 1]\n",
    "gen_imgs = 0.5 * gen_imgs + 0.5\n",
    "\n",
    "# Set image grid\n",
    "fig, axs = plt.subplots(image_grid_rows,\n",
    "                        image_grid_columns,\n",
    "                        figsize=(10, 20),\n",
    "                        sharey=True,\n",
    "                        sharex=True)\n",
    "\n",
    "cnt = 0\n",
    "for i in range(image_grid_rows):\n",
    "    for j in range(image_grid_columns):\n",
    "        # Output a grid of images\n",
    "        axs[i, j].imshow(gen_imgs[cnt, :, :, 0], cmap='gray')\n",
    "        axs[i, j].axis('off')\n",
    "        axs[i, j].set_title(\"Digit: %d\" % labels_to_generate[cnt])  ## NEW\n",
    "        cnt += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
